Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system comprising: a processor; a data bus coupled to the processor; and a computer-usable medium embodying computer program code, the computer-usable medium being coupled to the data bus, the computer program code used for identifying verifiable statements and comprising instructions executable by the processor and configured for: receiving a text containing a plurality of statements, the text received by a system configured to parse text input; processing the text to parse the plurality of statements into segmented statements; processing the segmented statements to identify individual segmented statements that are verifiable, each individual segmented statement that is verifiable being identified as a verifiable statement, the verifiable statement being a subset of factual utterances that can be checked and verified or falsified via an authoritative source, the verifiable statement comprising an objective claim that is amenable to external verification, the identification performed by the system; processing the segmented statements to extract features associated with the statements via a feature extraction, the feature extraction simplifying an amount of resources required to accurately describe a large set of data, the feature extraction performed by the system, the features being extracted via the feature extraction comprising semantic features, the semantic features being represented in a notation form to express an existence or non-existence of pre-established semantic properties, the feature extraction being performed using a plurality of named entities, each of the plurality of named entities referring to a place holder for a particular feature or piece of information, the feature extraction receiving information regarding language knowledge, the information regarding language knowledge including information regarding magnitude, information regarding velocity and information regarding quantified items, the information regarding language knowledge being used when performing the feature extraction to classify the segmented statements as verifiable or not verifiable; receiving a training text comprising annotated verifiable statements, the annotated verifiable statements being verified by a statement verification service; and processing the extracted features and the annotated verifiable statements to generate a verifiable statement classification model, the generation performed by the system, the verifiable statement classification model enabling automation of identification and classification of statements that contain objective claims that are amenable to external verification, and wherein, named entity phrases are used to construct grammars, regular expressions and the verifiable statement classification model, each named entity phrase being associated with a list of possible phrases.
Natural Language Processing, Information Extraction. This invention addresses the challenge of automatically identifying and classifying statements within a text that are objective and can be verified or falsified using external sources. The system receives text input and parses it into individual statements. It then processes these segmented statements to identify which ones are verifiable. A verifiable statement is defined as a factual utterance that can be checked against an authoritative source, presenting an objective claim amenable to external verification. The system employs feature extraction to simplify the representation of statements, reducing the resources needed for accurate analysis. This feature extraction focuses on semantic features, which are represented in a notation that indicates the presence or absence of pre-established semantic properties. The extraction process utilizes named entities, acting as placeholders for specific features or information. It incorporates language knowledge, including details about magnitude, velocity, and quantified items, to help classify statements as verifiable or not. Furthermore, the system is trained using a training text containing annotated verifiable statements, where these statements have been verified by a statement verification service. By processing the extracted features from the input text and the annotated verifiable statements from the training data, the system generates a verifiable statement classification model. This model automates the identification and classification of statements containing objective claims that can be externally verified. Named entity phrases are instrumental in constructing grammars, regular expressions, and the classification model itself, with each phrase linked to a list of
2. The system of claim 1 , wherein individual extracted features correspond to at least one member of the set of: sentiment; verbs; verb tense; nouns; proper nouns; magnitude; velocity; importance; quantified items; quantitative comparison operators; and reference.
This invention relates to a system for extracting and analyzing features from text data to derive meaningful insights. The system addresses the challenge of processing unstructured text to identify and categorize key linguistic and contextual elements that can be used for further analysis, such as sentiment analysis, content classification, or decision-making. The system extracts individual features from text, where each feature corresponds to at least one of the following categories: sentiment, verbs, verb tense, nouns, proper nouns, magnitude, velocity, importance, quantified items, quantitative comparison operators, or reference. Sentiment analysis determines the emotional tone of the text, while verbs and verb tenses help identify actions and their timing. Nouns and proper nouns provide context about entities and specific references. Magnitude and velocity assess the scale and rate of change in the text, while importance highlights critical information. Quantified items and quantitative comparison operators enable numerical analysis, and references help establish connections between different parts of the text or external sources. By categorizing these features, the system enables structured analysis of unstructured text, improving applications such as natural language processing, automated content summarization, and decision support systems. The extracted features can be used individually or in combination to enhance accuracy and relevance in various analytical tasks.
3. The system of claim 1 , wherein: a machine learning algorithm is used by the system to perform the generation of the verifiable statement classification model.
A system for generating verifiable statement classification models addresses the challenge of accurately classifying statements in a way that ensures their validity and reliability. The system leverages machine learning to create a model capable of distinguishing between verifiable and non-verifiable statements. The machine learning algorithm processes input data, which may include historical statements, metadata, and contextual information, to train the classification model. The trained model then evaluates new statements, assigning a classification based on learned patterns and features. This approach improves the accuracy and efficiency of statement verification, reducing the need for manual review and minimizing errors. The system can be applied in various domains, such as legal, financial, or media, where the authenticity and reliability of statements are critical. By automating the classification process, the system enhances decision-making and ensures compliance with verification standards. The use of machine learning allows the model to adapt and improve over time as it encounters more data, further refining its classification capabilities.
4. The system of claim 3 , wherein: the system performs the identification by referencing the verifiable statement classification model.
A system for classifying verifiable statements in digital content analyzes text to determine whether statements are verifiable or unverifiable. The system processes input text, extracts statements, and applies a classification model to assess verifiability. The classification model is trained on labeled data to distinguish verifiable statements from unverifiable ones, using features such as statement structure, context, and source reliability. The system outputs a classification result for each statement, enabling downstream applications like fact-checking or content moderation. The classification model may be updated periodically to improve accuracy. The system integrates with content platforms to automatically evaluate statements in real-time or batch processing. The technology addresses the challenge of efficiently identifying verifiable statements in large-scale digital content, improving trust and reliability in information dissemination. The system may also include preprocessing steps to normalize text and post-processing to refine classifications. The classification model may be a machine learning model, such as a neural network, trained on historical data of verified and unverified statements. The system ensures scalability and adaptability to different domains and languages.
5. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for: receiving a text containing a plurality of statements, the text received by a system configured to parse text input; processing the text to parse the plurality of statements into segmented statements; processing the segmented statements to identify individual segmented statements that are verifiable, the verifiable statement being a subset of factual utterances that can be checked and verified or falsified via an authoritative source, each individual segmented statement that is verifiable being identified as a verifiable statement, the verifiable statement comprising an objective claim that is amenable to external verification, the identification performed by the system; processing the segmented statements to extract features associated with the statements via a feature extraction, the feature extraction simplifying an amount of resources required to accurately describe a large set of data, the feature extraction performed by the system, the features being extracted via the feature extraction comprising semantic features, the semantic features being represented in a notation form to express an existence or non-existence of pre-established semantic properties, the feature extraction being performed using a plurality of named entities, each of the plurality of named entities referring to a place holder for a particular feature or piece of information, the feature extraction receiving information regarding language knowledge, the information regarding language knowledge including information regarding magnitude, information regarding velocity and information regarding quantified items, the information regarding language knowledge being used when performing the feature extraction to classify the segmented statements as verifiable or not verifiable; receiving a training text comprising annotated verifiable statements, the annotated verifiable statements being verified by a statement verification service; and processing the extracted features and the annotated verifiable statements to generate a verifiable statement classification model, the generation performed by the system, the verifiable statement classification model enabling automation of identification and classification of statements that contain objective claims that are amenable to external verification; and, wherein named entity phrases are used to construct grammars, regular expressions and the verifiable statement classification model, each named entity phrase being associated with a list of possible phrases.
This invention relates to automated text analysis for identifying verifiable factual statements. The system processes text containing multiple statements, parsing them into segmented statements. It then identifies verifiable statements—those that can be checked or disproven via authoritative sources—by analyzing semantic features and language knowledge, including magnitude, velocity, and quantified items. Named entities are used to extract and classify these features, simplifying data representation. The system also receives training text with pre-annotated verifiable statements, verified by an external service, to generate a classification model. This model automates the identification and classification of objective claims that can be externally verified. Named entity phrases are used to construct grammars and regular expressions, each associated with possible phrases, enhancing the accuracy of statement classification. The approach reduces computational resources while improving the precision of identifying verifiable factual content in text.
6. The non-transitory, computer-readable storage medium of claim 5 , wherein individual extracted features correspond to at least one member of the set of: sentiment; verbs; verb tense; nouns; proper nouns; magnitude; velocity; importance; quantified items; quantitative comparison operators; and reference.
This invention relates to natural language processing (NLP) systems that extract and analyze features from text data to improve information retrieval, sentiment analysis, or other text-based applications. The problem addressed is the need for more precise and context-aware feature extraction from unstructured text, where traditional methods may overlook nuanced linguistic elements that impact meaning. The system processes text by identifying and categorizing specific linguistic features, including sentiment, verbs, verb tenses, nouns, proper nouns, magnitude, velocity, importance, quantified items, quantitative comparison operators, and references. These features are extracted to enhance the system's ability to interpret context, relationships, and intent within the text. For example, verb tenses may indicate temporal context, while quantified items and comparison operators help assess numerical relationships. Proper nouns and references aid in entity recognition and disambiguation. The extracted features are then used to improve downstream tasks such as search relevance, sentiment classification, or knowledge graph construction. The invention ensures that the extracted features are stored in a non-transitory, computer-readable storage medium, enabling efficient retrieval and further processing. This approach allows for more accurate and context-aware text analysis compared to systems that rely on simpler keyword-based or bag-of-words methods. The system is particularly useful in applications requiring deep semantic understanding, such as legal document analysis, financial reporting, or customer feedback processing.
7. The non-transitory, computer-readable storage medium of claim 5 , wherein: a machine learning algorithm is used by the system to perform the generation of the verifiable statement classification model.
A system for generating a verifiable statement classification model uses machine learning algorithms to analyze input data and produce a model capable of classifying statements as verifiable or unverifiable. The system processes input data, which may include text statements, metadata, or other relevant information, to train the machine learning algorithm. The algorithm identifies patterns and features in the data that correlate with verifiability, such as the presence of verifiable sources, factual consistency, or contextual clues. The trained model is then used to classify new statements, providing an output that indicates whether each statement can be verified based on the learned patterns. This approach automates the verification process, reducing the need for manual review and improving efficiency in applications like fact-checking, content moderation, or data validation. The system may also incorporate additional techniques, such as natural language processing, to enhance the accuracy of the classification model. The machine learning algorithm is trained on labeled datasets where statements are pre-classified as verifiable or unverifiable, allowing the model to learn from examples and generalize to new, unseen data. The resulting model can be deployed in various environments, including web applications, enterprise systems, or research tools, to assist in verifying the accuracy of statements in real-time or batch processing scenarios.
8. The non-transitory, computer-readable storage medium of claim 7 , wherein: the system performs the identification by referencing the verifiable statement classification model.
A system for analyzing verifiable statements in digital content uses machine learning to classify and identify such statements. The system processes digital content, such as text or multimedia, to detect statements that can be verified against external sources. A verifiable statement classification model, trained on labeled data, determines whether a statement is verifiable or not. The model evaluates factors like specificity, source reliability, and contextual clues to make this determination. Once identified, the system may flag, categorize, or extract these verifiable statements for further analysis or fact-checking. The system can be integrated into content management platforms, social media, or news aggregation tools to enhance transparency and accuracy. The classification model improves over time with additional training data, adapting to new types of verifiable statements and evolving digital content formats. This approach helps users distinguish between verifiable and unverified information, reducing misinformation spread. The system may also prioritize verifiable statements for human review or automated fact-checking workflows.
9. The non-transitory, computer-readable storage medium of claim 5 , wherein the computer executable instructions are deployable to a client system from a server system at a remote location.
A system and method for deploying computer-executable instructions from a remote server to a client system. The invention addresses the challenge of efficiently distributing software updates, applications, or configurations to client devices over a network. The solution involves storing executable instructions on a non-transitory computer-readable storage medium, where these instructions can be transmitted from a server system located at a remote site to a client system. The server system manages the deployment process, ensuring that the instructions are properly delivered and executed on the client system. This approach enables centralized control over software distribution, reduces manual installation efforts, and ensures consistency across multiple client devices. The system may include additional features such as authentication, error handling, and progress tracking to enhance reliability and security during deployment. The invention is particularly useful in enterprise environments where maintaining up-to-date software across numerous devices is critical. By leveraging remote deployment, organizations can streamline software management, minimize downtime, and improve operational efficiency.
10. The non-transitory, computer-readable storage medium of claim 5 , wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis.
A system and method for providing computer-executable instructions on an on-demand basis from a service provider to a user. The invention addresses the need for flexible, scalable access to computational resources without requiring users to maintain their own infrastructure. The system includes a storage medium containing executable instructions that are delivered to users as needed, eliminating the need for local installation or maintenance. The service provider manages the distribution, ensuring that users can access the latest versions of the software without manual updates. This on-demand model reduces costs for users by shifting the burden of hardware and software maintenance to the service provider, while also enabling rapid deployment of new features or updates. The system may include additional components such as authentication mechanisms, usage tracking, and billing systems to manage access and ensure proper compensation for the service provider. The on-demand delivery model is particularly useful in cloud computing environments, where users require temporary or variable access to specialized software tools without long-term commitments. The invention improves efficiency by streamlining software distribution and reducing the complexity of managing computational resources.
Unknown
January 2, 2018
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